p.149 Think of the evaluation function as providing a measure of the intrinsic value of objects
in the search space and the objective of search to find the highest-value object in the search space.
p.162 Simple evaluation functions are easy to come by. In the case of chess, we might assign
a value to each piece with the more powerful pieces like rooks and queens assigned higher values. Let w(n) be the sum of the
values of all the white pieces on the board in the state corresponding to n, and b(n) be the sum of the values of all black
pieces on the board. If the maximizer is controlling the white pieces, then a crude approximation of the value of node [n]
can be computed as follows:
e(n) = (w(n)-b(n)) / (w(n)+b(n))
This evaluation function for chess does not account for positional advantages. Obviously, there is little
advantage in having more pieces than your opponent if your king is in jeopardy and losing is inevitable. Calculating
positional advantages is generally more costly from a computational standpoint than simply adding up the value of pieces on
the board. Given a finite amount of time, there is a tradeoff between time spent searching deeper in the tree and
time spent in evaluating individual nodes.
